Contextual priority based multimedia modification

ABSTRACT

A computer-implemented method for multimedia modification is disclosed. The computer-implemented method includes classifying one or more objects detected within a user&#39;s field of view through an augmented reality environment. The computer-implemented method further includes determining a context of the user based, at least in part, on the one or more classified objects detected within the user&#39;s field of view. The computer-implemented method further includes generating a priority score for the one or more classified objects based, at least in part, on the context of the user. The computer-implemented method further includes modifying an object detected within the user&#39;s field of view based, at least in part, on the priority score of the object.

BACKGROUND

The present invention relates generally to the field of video analysis,and more particularly to, contextual priority video analysis forgenerative adversarial network (GAN) based video modification.

Video content analysis or video content analytics, also known as videoanalysis or video analytics, is the automatic analysis of videos todetect and determine temporal and spatial events. Video motion analysisis a technique used to obtain information about moving objects fromvideo. Object detection is a computer technology related to computervision and image processing that deals with detecting the location ofobjects of a certain class in digital images and videos. Objectdetection includes classification and regression based algorithms.Classification based algorithms include selecting a particular regionfrom the image, and then classifying the region using convolutionalneural networks, such as region-based convolutional neural networks(RCNN), Fast-RCNN, and Faster-RCNN. Regression based algorithms includegenerating classes and bounding boxes for the whole image, such as YouOnly Look Once (YOLO).

A GAN (Generative Adversarial Network) is a supervised learning problemwith two sub-models known as the generator model and the discriminatormodel. A generator model is trained to re-create and generate thedetected objects in the given input surrounding with a better visibilityand reality. A discriminator model is trained to classify the objects aseither real (from the domain) or fake (generated). It is provided withground truth images of the objects during training so that it candifferentiate the real from the fake ones. The two models are trainedtogether in a zero-sum game, adversarial, until the discriminator modelis fooled about half the time, meaning the generator model is generatingplausible objects.

SUMMARY

According to one embodiment of the present invention, acomputer-implemented method for multimedia modification is disclosed.The computer-implemented method includes classifying one or more objectsdetected within a user's field of view through an augmented realityenvironment. The computer-implemented method further includesdetermining a context of the user based, at least in part, on the one ormore classified objects detected within the user's field of view. Thecomputer-implemented method further includes generating a priority scorefor the one or more classified objects based, at least in part, on thecontext of the user. The computer-implemented method further includesmodifying an object detected within the user's field of view based, atleast in part, on the priority score of the object.

According to another embodiment of the present invention, a computerprogram product for multimedia modification is disclosed. The computerprogram product includes one or more computer readable storage media andprogram instructions stored on the one or more computer readable storagemedia. The program instructions include instructions to classify one ormore objects detected within a user's field of view through an augmentedreality environment. The program instructions further includeinstructions to determine a context of the user based, at least in part,on the one or more classified objects detected within the user's fieldof view. The program instructions further include instructions togenerate a priority score for the one or more classified objects based,at least in part, on the context of the user. The program instructionsfurther include instructions to modify an object detected within theuser's field of view based, at least in part, on the priority score ofthe object.

According to another embodiment of the present invention, a computersystem for multimedia modification is disclosed. The computer systemincludes one or more computer processors, one or more computer readablestorage media, and computer program instructions, the computer programinstructions being stored on the one or more computer readable storagemedia for execution by the one or more computer processors. The programinstructions include instructions to classify one or more objectsdetected within a user's field of view through an augmented realityenvironment. The program instructions further include instructions todetermine a context of the user based, at least in part, on the one ormore classified objects detected within the user's field of view. Theprogram instructions further include instructions to generate a priorityscore for the one or more classified objects based, at least in part, onthe context of the user. The program instructions further includeinstructions to modify an object detected within the user's field ofview based, at least in part, on the priority score of the object.

BRIEF DESCRIPTION OF DRAWINGS

The drawings included in the present disclosure are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 is a block diagram of a network computing environment for acontextual image modification program 101, generally designated 100, inaccordance with at least one embodiment of the present invention.

FIG. 2 is a flow chart diagram depicting operational steps forcontextual image modification program 101, generally designated 200, inaccordance with at least one embodiment of the present invention.

FIG. 3 is a block diagram depicting components of a computer, generallydesignated 300, suitable for executing a contextual image modificationprogram 101, in accordance with at least one embodiment of the presentinvention.

FIG. 4 is a block diagram depicting a cloud computing environment 50 inaccordance with at least one embodiment of the present invention.

FIG. 5 is block diagram depicting a set of functional abstraction modellayers provided by cloud computing environment 50 depicted in FIG. 4 inaccordance with at least one embodiment of the present invention.

While the embodiments described herein are amenable to variousmodifications and alternative forms, specifics thereof have been shownby way of example in the drawings and will be described in detail. Itshould be understood, however, that the particular embodiments describedare not to be taken in a limiting sense. On the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the disclosure.

DETAILED DESCRIPTION

The present invention relates generally to the field of video analysis,and more particularly to, contextual priority video analysis forgenerative adversarial network (GAN) based video modification.

Oftentimes, the terms image classification and object detection are usedinterchangeably. However, these two image analysis algorithms have quitedifferent purposes. In general, image classification is used to classifyan image as pertaining to a particular category, while object detectionis used to identify the location of a particular object within an image.Although image classification and object detection are used fordifferent purposes, there is some overlap between the two. For example,when there are crowded or overlapping objects in an image or video, aperson might be interested in seeing only one object of particularinterest to the viewer. Similarly, there may be instances where a personis not interested in seeing an object of particular disinterest to theviewer. In some cases, the object or the characteristics that arerequired to be seen by the user might be too small with respect to thefull image. In these situations, a better performance is achieved withobject detection instead of image classification even if the viewer isnot interested in identifying the exact location or number of instancesof an object.

Embodiments of the present invention recognize that while performing anactivity, the visual surrounding of a person can be of utmostimportance. For a particular contextual situation, different objects inthe surrounding area of a person may be more or less important. However,as the contextual situation of the person changes, so too may thelocation or position of objects relative to the person's environment.However, as the location or position of objects relative to the person'senvironment changes, the particular objects of interest or havingpriority over other objects may become blocked by other objects, whichultimately may inhibit a person from properly or safely performing agiven task. The overlapping of objects in a digital image or video frameis referred to as occlusions. Occlusions caused by objects of the sameclass is called intra-class occlusion, also referred to as crowdocclusion.

Current image analysis algorithms focus on detecting static or movingoverlapping objects and generating images using a GAN by removingocclusions. However, embodiments of the present invention recognizedifferent objects may have different priority levels to the viewer basedon the context of the viewer. In different contexts, different object inthe surrounding environment of the user may get prioritized, and basedon a change in the context, the priority of the object can also bechanged. For example, a user's AR glasses in different contexts such asfor work, shopping, and entertainment with the same objects havingdifferent priority based on the context of the user wearing the ARglasses. Further, a person may be interested in only seeing part of adigital image or video. For example, when watching a movie, one user mayenjoy seeing a ghost in a scene while another user may not.

Embodiments of the present invention advantageously provide for animproved image analysis algorithm that contextually prioritizes objectsin a digital image or video based on the context of a person. Accordingto an embodiment of the present invention, an artificial intelligence(AI) system is employed to analyze the contextual situation of a digitalimage, video, augmented reality environment, and or virtual realityenvironment to generate a priority score for one or more detectedobjects. In an embodiment, detected objects in persons field of view areassigned a priority rating or score based on the context the person andregistered priority data associated with a particular person. In anembodiment, a priority score for an object is generated based, at leastin part, on one or more of the relative position of an individual withrespect to different objects in the persons field of view, the types ofobjects in the persons field of view, the relative direction of movementwith respect to different object in the persons view of view, a person'svisual focus with respect to objects in the persons field of view, adegree of visibility of objects within a person's field of view. In anembodiment, the priority score of an object is recalculated based on oneor more identified precautions taken by an individual while engaged in aparticular contextual situation. For example, if a person is takingsomething out of the oven and they are wearing oven mitts. Embodimentsof the present invention further advantageously utilize a GAN to modifyan object based on one or more registered user preferences if a priorityscore of an object is either below a predetermined threshold or above apredetermined threshold.

In an embodiment, if a detected object has a priority score above apredetermined threshold, a GAN is used to modify the object having thepriority score above a predetermined threshold. For example, if adetected object is not clearly visible to a person present within aparticular contextual environment (e.g., a person is working in amachine shop and there is high speed rotating chain that is not clearlyvisible when moving at such high speeds), and the detected object has apriority score above a predetermined threshold, a GAN is used to displaya modified object via augmented reality (AR) glasses so that theindividual can clearly visualize the object.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suit-able combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention will now be described in detail with reference tothe Figures. FIG. 1 is a functional block diagram of a network computingenvironment suitable for a contextual image modification program 101,generally designated 100, in accordance with at least one embodiment ofthe present invention. In an embodiment, network computing environment100 may be provided by cloud computing environment 50, as depicted anddescribed with reference to FIG. 4 , in accordance with at least oneembodiment of the present invention. FIG. 1 provides an illustration ofonly one implementation and does not imply any limitations with regardto the environments in which different embodiments may be implemented.Many modifications to the depicted environment may be made by thoseskilled in the art without departing from the scope of the presentinvention as recited by the claims.

Network computing environment 100 includes user device 110, server 120,and storage device 130 interconnected over network 140. User device 110may represent a computing device of a user, such as a laptop computer, atablet computer, a netbook computer, a personal computer, a desktopcomputer, a personal digital assistant (PDA), a smart phone, a wearabledevice (e.g., smart glasses, smart watches, e-textiles, AR glasses, ARheadsets, etc.), or any programmable computer systems known in the art.In general, user device 110 can represent any programmable electronicdevice or combination of programmable electronic devices capable ofexecuting machine readable program instructions and communicating withserver 120, storage device 130 and other devices (not depicted) via anetwork, such as network 140. User device 110 can include internal andexternal hardware components, as depicted and described in furtherdetail with respect to FIG. 3 .

User device 110 further includes user interface 112 and application 114.User interface 112 is a program that provides an interface between auser of an end user device, such as user device 110, and a plurality ofapplications that reside on the device (e.g., application 114). A userinterface, such as user interface 112, refers to the information (suchas graphic, text, and sound) that a program presents to a user, and thecontrol sequences the user employs to control the program. A variety oftypes of user interfaces exist. In one embodiment, user interface 112 isa graphical user interface. A graphical user interface (GUI) is a typeof user interface that allows users to interact with electronic devices,such as a computer keyboard and mouse, through graphical icons andvisual indicators, such as secondary notation, as opposed to text-basedinterfaces, typed command labels, or text navigation. In computing, GUIswere introduced in reaction to the perceived steep learning curve ofcommand-line interfaces which require commands to be typed on thekeyboard. The actions in GUIs are often performed through directmanipulation of the graphical elements. In another embodiment, userinterface 112 is a script or application programming interface (API).

Application 114 can be representative of one or more applications (e.g.,an application suite) that operate on user device 110. In an embodiment,application 114 is representative of one or more applications (e.g.,augmented reality (AR) application, virtual reality (VR) application,streaming application, multimedia application) located on user device110. In various example embodiments, application 114 can be anapplication that a user of user device 110 utilizes to view objects inreal-time. In various example embodiments, application 114 can be anapplication that displays multimedia such as images, videos, andrecordings. In an embodiment, application 114 can be a client-sideapplication associated with a server-side application running on server120 (e.g., a client-side application associated with contextual imagemodification program 101). In an embodiment, application 114 can operateto perform processing steps of contextual image modification program 101(i.e., application 114 can be representative of contextual imagemodification program 101 operating on user device 110).

Server 120 is configured to provide resources to various computingdevices, such as user device 110. For example, server 120 may hostvarious resources, such as motion and object detection module 122 thatare accessed and utilized by a plurality of devices participating incontextual priority image modification. In various embodiments, server120 is a computing device that can be a standalone device, a managementserver, a web server, an application server, a mobile device, or anyother electronic device or computing system capable of receiving,sending, and processing data. In an embodiment, server 120 represents aserver computing system utilizing multiple computers as a server system,such as in a cloud computing environment. in an embodiment, server 120represents a computing system utilizing clustered computers andcomponents (e.g. database server computer, application server computer,web server computer, webmail server computer, media server computer,etc.) that act as a single pool of seamless resources when accessedwithin network computing environment 100. In general, server 120represents any programmable electronic device or combination ofprogrammable electronic devices capable of executing machine readableprogram instructions and communicating with each other, as well as withuser device 110, storage device 130, and other computing devices (notshown) within network computing environment 100 via a network, such asnetwork 140.

In an embodiment, server 120 includes contextual image modificationprogram 101, which further includes motion and object detection module122. In an embodiment, motion and object detection module 122 is acomputer algorithm used to determine the motion, distance, and type ofobject within a field of view of a person. For example, motion andobject detection module 122 determines there is a spinning wheel 5 feetaway from the user. In an embodiment, motion and object detection module122 detects moving objects with compensation and deep learning. In anembodiment, a convolutional neural network based method (YOLOx3-SOD) isemployed to detect all objects in an image or field of view of a person,by fusing the results obtained by motion detection and object detection.

In an embodiment, contextual image modification program 101 may beconfigured to access various data sources, such as a trained GAN 132,user object preference profile 134, and object scoring policies 136,which may include personal data, content, contextual data, orinformation that a user does not want to be processed. Personal dataincludes personally identifying information or sensitive personalinformation as well as user information, such as location tracking orgeolocation information. Processing refers to any operation, automatedor unautomated, or set of operations such as collecting, recording,organizing, structuring, storing, adapting, altering, retrieving,consulting, using, disclosing by transmission, dissemination, orotherwise making available, combining, restricting, erasing, ordestroying personal data. In an embodiment, contextual imagemodification program 101 enables the authorized and secure processing ofpersonal data. In an embodiment, contextual image modification program101 provides informed consent, with notice of the collection of personaldata, allowing the user to opt in or opt out of processing personaldata. Consent can take several forms. Opt-in consent can impose on theuser to take an affirmative action before personal data is processed.Alternatively, opt-out consent can impose on the user to take anaffirmative action to prevent the processing of personal data beforepersonal data is processed. In an embodiment, contextual imagemodification program 101 provides information regarding personal dataand the nature (e.g., type, scope, purpose, duration, etc.) of theprocessing. In an embodiment, contextual image modification program 101provides a user with copies of stored personal data. In an embodiment,contextual image modification program 101 allows for the correction orcompletion of incorrect or incomplete personal data. In an embodiment,contextual image modification program 101 allows for the immediatedeletion of personal data.

Server 120 may include components as depicted and described in detailwith respect to cloud computing node 10, as described in reference toFIG. 4 , in accordance with at least one embodiment of the presentinvention. Server 120 may include components, as depicted and describedin detail with respect to computing device 300 of FIG. 3 , in accordancewith at least one embodiment of the present invention.

in various embodiments, storage device 130 is a secure data repositoryfor a trained GAN, user profile information, and policies utilized byvarious applications and user devices of a user, such as user device110. Storage device 130 may be implemented using any volatile ornon-volatile storage media known in the art for storing data. Forexample, storage device 130 may be implemented with a tape library,optical library, one or more independent hard disk drives, multiple harddisk drives in a redundant array of independent disks (RAID),solid-state drives (SSD), random-access memory (RAM), and any possiblecombination thereof. Similarly, storage device 130 may be implementedwith any suitable storage architecture known in the art, such as arelational database, an object-oriented database, or one or more tables.

In an embodiment, storage device 130 includes GAN 132, user objectpreference profile 134, and object scoring policies 136. In anembodiment, GAN 132 is utilized to regenerate one or more priorityobjects based on an objects priority score to provide a bettervisibility of the one or more priority objects. In an embodiment, GAN132 is trained using previously classified or tagged objects associatedwith a particular class of objects. In an embodiment, GAN 132 is trainedto re-create and generate the detected priority objects in a givencontextual environment with improved visibility. In an embodiment, GAN132 is trained to classify priority objects as either real (from thedomain) or fake (generated). In an embodiment, GAN 132 is provided withground truth images of priority objects during training in order todifferentiate the real from the fake ones. In an embodiment, GAN 132 istrained in a zero-sum game, adversarial, until the discriminator modelis fooled about half the time, meaning the generator model is generatingplausible priority objects.

In an embodiment, user object preference profile 134 comprisesinformation relating to the user, such as users occupation, profile,preferences, context, and similar. Profile comprises the users job role,specialization, historical preferences, and position to surrounding.Context comprises the circumstances that form the setting for an event,statement, or idea, and in terms of which it can be fully understood andassessed. In an embodiment, different users may be interested in viewingdifferent objects even from the same view, video, or image. In anembodiment, contextual image modification program 101 accessesinformation in user object preference profile 134 to determine one ormore users preferences and analyzes the context of the surroundings inthe user's view, video, or image. In an example, during an operation acardiologist is highly interested in the heart as a priority objectwhile the anesthesiologist is highly interested in the instrumentsdisplaying the vitals. In an embodiment, contextual image modificationprogram 101 gathers information from user object preference profile 134to generate a priority score for more or more objects in the user'sfield of view. For example, while cooking in the kitchen, a chef willhave a high priority score for the food items and the dishwasher willhave a high priority score for dishes and other cookware. In anembodiment, contextual image modification program 101 identifies one ormore precautions taken by a user and generates a priority score of anobject based on the precaution taken and stores this information in userobject preference profile 134. For example, if user takes a precautionof wearing steal toe boots while on a construction site, contextualimage modification program 101 generates a priority score based, atleast in part, on the precaution and identified objects and stores thisinformation in user object preference profile 134.

In an embodiment, object scoring policies 136 includes a dynamic set ofrules for determining one or more priority objects based, at least inpart, on user object preference profile 134. In an embodiment, objectscoring policies 136 includes information describing differentdecision-making actions by contextual image modification program 101depending on the particular contextual environment of the user, theparticular objects and priority scores assigned to objects detected inthe contextual environment, and information included in a user objectpreference profile 134. In an embodiment, a particular policy isselected based, at least in part, on matching one or more of thedistances between the priority object and the user, the motion of thepriority object, the precautions the user has taken, the usersoccupation, the users preferences, the type of priority object, and thepriority object score to a policy.

In an embodiment, contextual image modification program 101 regeneratesan image based, at least in part, on contextually prioritizing one ormore objects. In an embodiment, contextual image modification program101 receives user input for the user's profile. In an embodiment, theuser input includes preferences, the user's occupation, context,profile, precautions, historical preferences, or other informationrelated to the user. For example, contextual image modification program101 receives user input that user A is a cardiologist. In anotherexample, contextual image modification program 101 receives user inputthat user B does not prefer to see scary objects in a movie. In yetanother example, contextual image modification program 101 receives userinput that user C took the precaution of wearing metal gloves.

In an embodiment, contextual image modification program 101 identifiesone or more objects in a person's field of view. In an embodiment,contextual image modification program 101 identifies one or more objectsfrom a real-time image, video, AR environment or VR environment. In anembodiment, contextual image modification program 101 identifies andclassifies one or more objects within the user's field of view throughan augmented reality device using video content analysis, video contentanalytics, object analysis, or video motion analysis. For example, userA is wearing AR glasses and contextual image modification program 101identifies and classifies an engine, transmission, and radiator from thereal-time image data viewed through the AR glasses. In another example,user B is watching a movie and contextual image modification program 101identifies a desk, a person, and a dog from the video frames of themovie.

In an embodiment, contextual image modification program 101 determinescontextual data in relation to the one or more detected objects in aperson's field of view. In an embodiment, contextual image modificationprogram 101, determines a context of the user based, at least in part,on the one or more classified objects detected within the user's fieldof view In an embodiment, contextual image modification program 101determines the context of the image, video, AR environment, or VRenvironment based on the one or more detected objects. In an embodiment,contextual image modification program 101 determines contextual data inrelation to the one or more detected objects based, at least in part, onuser profile information. For example, contextual image modificationprogram 101 identifies the user, through their AR glasses, is viewing anengine, transmission, and radiator, and a user profile associated withthe user includes information that the user is a mechanic, thencontextual image modification program 101 determines the context is“user is looking under the hood of a car.”

In an embodiment, contextual image modification program 101 determinescontextual data of a person's field of view based, at least in part on,the position, direction, area, location, visibility, movement, activityarea, other detected objects and location of where the user is focusing.For example, contextual image modification program 101 determines adangerous context when the user is 6 inches away from a fast spinningwheel verses when the user is 16 feet away from the fast spinning wheel.In another example, contextual image modification program 101 determinesthe context to be a kitchen where the user is cooking based on the userslocation surroundings of an oven, dishwasher, sink and counter and theusers eyes focusing on a bowl, mixing tool, flour, and eggs. In anembodiment, contextual image modification program 101 determines one ormore priority objects. In the previous example, based on the location ofwhere the user is focusing, contextual image modification program 101determines the mixing tool and bowl with the ingredients are thepriority objects.

In an embodiment, contextual image modification program 101 generates apriority score for the one or more detected objects within a person'sfield of view. In an embodiment, contextual image modification program101 generates a priority score for the one or more classified objectsbased, at least in part, on the context of the user. In an embodiment,contextual image modification program 101 generates a priority scorebased, at least in part, on information included in a user profileassociated with a particular person. For example, user profile includeshistorical preferences that a user is typically interested in theradiator wires when looking under the hood of a car. In this example,contextual image modification program 101 generates a higher priorityscore for the radiator wires than the engine when the user is lookingunder the hood of a car. In an embodiment, contextual image modificationprogram 101 generates a priority score based on the dangers andprecautions taken by the user. For example, contextual imagemodification program 101 determines the user is wearing protectiveeyewear as a precaution and determines the user is 5 feet from a sawmillwith wood dust. In this example, since the user is wearing protectiveeyewear, contextual image modification program determines a lowerpriority score than if a user was not wearing protective eyewear. In anembodiment, contextual image modification program 101 generates apriority score based on the eye movement of the user. For example, ifcontextual image modification program 101 determines that a user islooking at the top left of their field of view, contextual imagemodification program 101 generates a higher priority score for objectsin the top left of the user's field of view compared to the objects inthe bottom right of the users field of view. In an embodiment,contextual image modification program 101 generates a priority scorebased on the visibility of the priority object. In an embodiment, themore unclear or blocked the priority object is from being visible, thehigher the priority score assigned to the object. For example, if thepriority object is obfuscated by fog, contextual image modificationprogram 101 generates a high priority score for the priority object. Inan embodiment, contextual image modification program 101 generates adegree of visibility based, at least in part, on the percentage of thepriority object visible. In an embodiment, the lower the degree ofvisibility, the higher the priority score assigned to the object. In anembodiment, if a degree of visibility is below a predeterminedthreshold, contextual image modification program 101 generates a highpriority score for the priority object. For example, if the priorityobject is 50% blocked by a secondary object, contextual imagemodification program 101 generates a high priority score for thepriority object.

In an embodiment, contextual image modification program 101 updates oralters the priority score based on a change in the user's environment orchange in the user's field of view. For example, if the user changesfocus from the top left to the top right of their field of view,contextual image modification program 101 lowers the priority score forthe objects in the top left and increases the priority score for objectsin the top right of their field of view. In another example, if thepriority object is a motor and is originally partially blocked byanother object, such as a radiator, and the radiator is later removed bythe user, contextual image modification program 101 lowers the priorityscore of the priority object based on the increased visibility thereof.

In an embodiment, contextual image modification program 101 selects apolicy, based, at least in part, on comparing a classification of anobject to one or more object modification policies associated with theuse. For example, if contextual image modification program 101 detects asaw mill, contextual image modification program 101 selects amodification policy based on the classification of “sharp objects.” Inan embodiment, contextual image modification program 101 selects apolicy, based, at least in part, on the particular contextualenvironment of the user, the particular objects and priority scoresassigned to objects detected in the contextual environment, andinformation included in a user profile. In an embodiment, a particularpolicy is selected based, at least in part, on matching one or more ofthe distance between the priority object and the user, the motion of thepriority object, the precautions the user has taken, the usersoccupation, the users preferences, the type of priority object, and thepriority object score to a policy.

In an embodiment, contextual image modification program 101 modifies oneor more objects based, at least in part, on the priority score. In anembodiment, contextual image modification program 101 modifies an objectdetected within the user's field of view based, at least in part, on thepriority score of the object. In an embodiment, contextual imagemodification program 101 modifies an object detected within the user'sfield of view based, at least in part, on the modification policyselected. For example, if the selected modification policy indicatesremoving an object from the users point of view, contextual imagemodification program 101 removes the object from the users point ofview. In an embodiment, contextual image modification program 101modifies one or more objects in an AR environment or VR environmentutilizing a GAN. In an embodiment, contextual image modification program101 utilizes a trained GAN to modify one or more priority objects basedon the priority score to provide better visibility of one or morepriority objects to the user. In an embodiment, the GAN networkgenerates an image that the lower priority objects are blurred orremoved. In an embodiment, an augmented border is overlaid, viaaugmented reality, around the lower priority objects to indicate theirpresence without obstructing view of the priority object. For example,contextual image modification program 101 recreates an image by removinga low priority object that is blocking a high priority and indicates thelow priority object by a dashed border to give better visibility to anobject of higher priority.

In an embodiment, contextual image modification program 101 removes anobject from the user's field of view within the augmented realityenvironment based, at least in part on, determining that a priorityscore of the object is below a predetermined threshold. For example, ifout of 10 the predetermined threshold to remove an object is a priorityscore of 4 or lower, contextual image modification program 101 removesan object from the user's field of view within the augmented realityenvironment for any object with a priority score of 4 or lower.

In an embodiment, contextual image modification program 101 removes anobject from the user's field of view within the augmented realityenvironment based, at least in part on, determining that a priorityscore of the object is above a predetermined threshold. For example, ifout of 10 the predetermined threshold to remove an object is a priorityscore of 7 or higher, contextual image modification program 101 removesan object from the user's field of view within the augmented realityenvironment for any object with a priority score of 7 or higher.

In an embodiment, contextual image modification program 101 and the GANgenerate a portion of a high priority object or entirely recreates ahigh priority object that cannot clearly be seen in the user's field ofview. For example, a portion of a high priority object that is blockedor obscured from the user's view is recreated based on thecharacteristics of the portion of the high priority that is visible tothe user. In an embodiment, contextual image modification program 101determines from a partial view of the obscured object what the obscuredobject is as a whole. For example, from a partial view of a lamp blockedby a sofa, contextual image modification program 101 is able todetermine the obscured object is a lamp.

In an embodiment, contextual image modification program 101 regeneratesthe image based on a context of the user (e.g., a particular activitybeing performed by the user) and identifying comparative priority ofdifferent objects. In an embodiment, contextual image modificationprogram 101 utilizes a trained GAN to regenerate the image to show ahigh priority object clearly and makes a low priority objecttransparent. In an embodiment, contextual image modification program 101compares two objects overlapping one another and determines the one witha higher priority score is more important than the object with a lowerpriority score. For example, if object A with a priority score of 5 isoverlapping object B with a priority score of 7, contextual imagemodification program 101 determines object B is more important andutilizes a trained GAN to regenerate the image to show object B andremove object A.

In an embodiment, contextual image modification program 101 identifiesprecautions taken by the user while performing an activity in aparticular context, and based on the level of precaution taken by theuser, recalculates the priority score of different objects, andremodifies a particular object using a GAN. For example, if the user iswearing metal gloves, during that time, a rotating saw will not causeany problems. However, if the user takes the metal gloves off,contextual image modification program 101 increases the priority scoreassociated with the object “rotating saw” and modifies the rotating sawblade using a GAN to clearly demonstrate to the user that the saw bladeis moving.

In an example, multiple workers trained in different specializations areremodeling a house and each one of them focus on their area of specialtyduring the remodeling. Based on each worker's profile, contextual imagemodification program 101 generates the tools and items of each workerspreference based on the workers background and specialty which wereoverlapping or not clear. Here the situational context varies based onthe workers profile, occupation, background, specialty, and position.For example, an electrician is interested in viewing the wires andelectric tools of the house while the painter is interested in viewingthe paint, walls, and other painting materials for the house.

In another example, different users might be interested in viewingdifferent content of the same video. Sometimes users do not want to seea specific content in a video. In such cases, where users of differentprofiles are gathered to view a common video, contextual imagemodification program 101 generates only the content that a user isinterested in. For example, user A and user B are watching the samemovie together while both wearing AR glasses and user A is scared ofghosts, while user B enjoys ghosts. In an embodiment, contextual imagemodification program 101 utilizes GAN enabled AR glasses to identify theobject which is not of priority and hides it from the user who does notwish to witness it in a video. Here, contextual image modificationprogram 101 generates a high priority score for the ghost for user A andgenerates a low priority score for user B based on the policy for eachuser and determined object. Contextual image modification program 101determines the ghost in the movie is of high priority to user A and lowpriority to user B. In an embodiment, contextual image modificationprogram 101 removes objects of high importance based, at least in part,on the users object preference profile. Contextual image modificationprogram 101 regenerates the images in the movie to remove ghosts foruser A. In an embodiment, contextual image modification program 101overlays the background of the scene to remove one or more objects froma user's view. Here, contextual image modification program 101 selects apolicy based on the context, priority level, priority object andoverlays the background of the scene to remove the ghost from the usersview. In an embodiment, contextual image modification program 101generates, using a GAN, a portion of a background of the user's field ofview corresponding to an area encompassed by the object and displays thegenerated portion of the background corresponding to the areaencompassed by the object within the augmented reality environment.Here, contextual image modification program 101 generates a portion ofthe background corresponding to the area encompassing the object anddisplays the generated portion over the object of the ghost in order toblock the ghost form the users field of view. In another embodiment,contextual image modification program 101 selects a different policybased on the context, priority level, priority object and overlays adifferent object over the priority object to remove one or more objectsfrom user A's view. Here, contextual image modification program 101overlays an image of a puppy to remove the ghost from the user A's view.However, contextual image modification program 101 does not regeneratethe image for user B because based on user B's user profile user Bprefers to see ghosts. Meaning, user A skips seeing a specific content(ghost) as per their profile's preference.

In an embodiment, contextual image modification program 101 determinesthe priority object is partially blocked and modifies the image byoverlaying the blocked portion of the object so that it is visible tothe user. For example, a high priority object is given a high priorityscore based on the type of object and user profile, but the highpriority object is blocked by a lower priority object. Here, contextualimage modification program 101 overlays the blocked portion of the highpriority object so that it is visible to the user.

FIG. 2 is a flow chart diagram depicting operational steps forcontextually regenerating multimedia based on context, generallydesignated 200, in accordance with at least one embodiment of thepresent invention. FIG. 2 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made by those skilledin the art without departing from the scope of the invention as recitedby the claims.

At step S202, contextual image modification program 101 identifies oneor more objects. In an embodiment, contextual image modification program101 identifies one or more objects by video content analysis, videocontent analytics, object analysis, or video motion analysis. In anembodiment, contextual image modification program 101 identifies one ormore objects in a digital image, video, AR environment, or VRenvironment.

At step S204, contextual image modification program 101 determinescontextual data with respect to the one or more detected objects. In anembodiment, contextual image modification program 101 determines thecontext of the digital image, video, AR environment, or VR environmentbased on the one or more determined objects. In an embodiment,contextual image modification program 101 determines contextual databased, at least in part, on the position, direction, area, location,visibility, movement, activity area, other detected objects, andlocation of where the user is focusing. In an embodiment, contextualimage modification program 101 determines contextual data based, atleast in part, on the user profile.

At step S206, contextual image modification program 101 generates apriority score for the one or more objects detected based, at least inpart, on the contextual data associated with the one or more detectedobjects. In an embodiment, contextual image modification program 101generates a priority score based, at least in part, on the user profile.In an embodiment, contextual image modification program 101 generates apriority score based on the one or more dangers detected and precautionstaken by the user. For example, if the user has taken a precautionagainst the danger, contextual image modification program 101 lowers thepriority score. In an embodiment, contextual image modification program101 generates a priority score based on the eye movement of the user. Inan embodiment, contextual image modification program 101 generates apriority score based on the visibility of the priority object. Forexample, if the priority object has a low visibility, contextual imagemodification program 101 generates a high priority score for thepriority object.

At step S208, contextual image modification program 101 modifies adetected object based, at least in part, on the priority score. In anembodiment, contextual image modification program 101 regenerates theimage based on user's context of the activity, identifying comparativepriority of different objects. In an embodiment, contextual imagemodification program 101 modifies an object detected within the user'sfield of view based, at least in part, on the modification policyselected. In an embodiment, contextual image modification program 101utilizes the GAN to regenerate the image to show a comparative priorityobject clearly and makes a comparatively less priority objecttransparent.

FIG. 3 is a block diagram depicting components of a computing device,generally designated 300, suitable for contextual image modificationprogram 101 in accordance with at least one embodiment of the invention.Computing device 300 includes one or more processor(s) 304 (includingone or more computer processors), communications fabric 302, memory 306including, RAM 316 and cache 318, persistent storage 308, which furtherincludes contextual image modification program 101, communications unit312, I/O interface(s) 314, display 322, and external device(s) 320. Itshould be appreciated that FIG. 3 provides only an illustration of oneembodiment and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

As depicted, computing device 300 operates over communications fabric302, which provides communications between computer processor(s) 304,memory 306, persistent storage 308, communications unit 312, andinput/output (I/O) interface(s) 314. Communications fabric 302 can beimplemented with any architecture suitable for passing data or controlinformation between processor(s) 304 (e.g., microprocessors,communications processors, and network processors), memory 306, externaldevice(s) 320, and any other hardware components within a system. Forexample, communications fabric 302 can be implemented with one or morebuses.

Memory 306 and persistent storage 308 are computer readable storagemedia. In the depicted embodiment, memory 306 includes random-accessmemory (RAM) 316 and cache 318. In general, memory 306 can include anysuitable volatile or non-volatile computer readable storage media.

Program instructions for contextual image modification program 101 canbe stored in persistent storage 308, or more generally, any computerreadable storage media, for execution by one or more of the respectivecomputer processor(s) 304 via one or more memories of memory 306.Persistent storage 308 can be a magnetic hard disk drive, a solid-statedisk drive, a semiconductor storage device, read-only memory (ROM),electronically erasable programmable read-only memory (EEPROM), flashmemory, or any other computer readable storage media that is capable ofstoring program instructions or digital information.

Media used by persistent storage 308 may also be removable. For example,a removable hard drive may be used for persistent storage 308. Otherexamples include optical and magnetic disks, thumb drives, and smartcards that are inserted into a drive for transfer onto another computerreadable storage medium that is also part of persistent storage 308.

Communications unit 312, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 312 can include one or more network interface cards.Communications unit 312 may provide communications through the use ofeither or both physical and wireless communications links. In thecontext of some embodiments of the present invention, the source of thevarious input data may be physically remote to computing device 300 suchthat the input data may be received, and the output similarlytransmitted via communications unit 312.

I/O interface(s) 314 allows for input and output of data with otherdevices that may operate in conjunction with computing device 300. Forexample, I/O interface(s) 314 may provide a connection to externaldevice(s) 320, which may be as a keyboard, keypad, a touch screen, orother suitable input devices. External device(s) 320 can also includeportable computer readable storage media, for example thumb drives,portable optical or magnetic disks, and memory cards. Software and dataused to practice embodiments of the present invention can be stored onsuch portable computer readable storage media and may be loaded ontopersistent storage 308 via I/O interface(s) 314. I/O interface(s) 314also can similarly connect to display 322. Display 322 provides amechanism to display data to a user and may be, for example, a computermonitor.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

FIG. 4 is a block diagram depicting a cloud computing environment 50 inaccordance with at least one embodiment of the present invention. Cloudcomputing environment 50 includes one or more cloud computing nodes 10with which local computing devices used by cloud consumers, such as, forexample, personal digital assistant (PDA) or cellular telephone 54A,desktop computer 54B, laptop computer 54C, and/or automobile computersystem 54N may communicate. Nodes 10 may communicate with one another.They may be grouped (not shown) physically or virtually, in one or morenetworks, such as Private, Community, Public, or Hybrid clouds asdescribed hereinabove, or a combination thereof. This allows cloudcomputing environment 50 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 54A-N shown in FIG. 4 are intended to beillustrative only and that computing nodes 10 and cloud computingenvironment 50 can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

FIG. 5 is block diagram depicting a set of functional abstraction modellayers provided by cloud computing environment 50 depicted in FIG. 4 inaccordance with at least one embodiment of the present invention. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 5 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and contextual image regenerator 96.

What is claimed is:
 1. A computer-implemented method for multimediamodification, the computer-implemented method comprising: classifyingone or more objects detected within a user's field of view through anaugmented reality environment; determining a context of the user based,at least in part, on the one or more classified objects detected withinthe user's field of view; generating a priority score for the one ormore classified objects based, at least in part, on the context of theuser; and modifying an object detected within the user's field of viewbased, at least in part, on the priority score of the object.
 2. Thecomputer-implemented method of claim 1, wherein modifying an objectdetected within the user's field of view includes: determining that apriority score of the object is below a predetermined threshold; andremoving the object from the user's field of view within the augmentedreality environment.
 3. The computer-implemented method of claim 2,wherein removing the object from the user's field of view furtherincludes: generating, using a generative adversarial network, a portionof a background of the user's field of view corresponding to an areaencompassed by the object; and displaying the generated portion of thebackground corresponding to the area encompassed by the object withinthe augmented reality environment.
 4. The computer-implemented method ofclaim 1, wherein modifying an object detected within the user's field ofview includes: determining that a priority score of the object is abovea predetermined threshold; determining that a portion of the object isblocked from the user's view by another object having a priority scorebelow the predetermined threshold; generating, using a generativeadversarial network, the portion of the object blocked from the user'sview; and displaying the generated portion of the object blocked fromthe user's view within the augmented reality environment.
 5. Thecomputer-implemented method of claim 1, wherein modifying an objecteddetected within the user's field of view is further based, at least inpart, on: comparing a classification of the object to one or more objectmodification policies associated with the user.
 6. Thecomputer-implemented method of claim 1, wherein generating a priorityscore for the one or more classified objects is further based, at leastin part, on: a distance between a classified object and the user, adegree of visibility of an object, a detected motion of an objectrelative to the user, a perceived danger associated with an object, oneor more detected precautions taken by the user with respect to aclassified object.
 7. The computer-implemented method of claim 1,wherein an object is modified within the augmented reality environmentusing a generative adversarial network.
 8. A computer program productfor multimedia modification, the computer program product comprising oneor more computer readable storage media and program instructions storedon the one or more computer readable storage media, the programinstructions including instructions to: classify one or more objectsdetected within a user's field of view through an augmented realityenvironment; determine a context of the user based, at least in part, onthe one or more classified objects detected within the user's field ofview; generate a priority score for the one or more classified objectsbased, at least in part, on the context of the user; and modify anobject detected within the user's field of view based, at least in part,on the priority score of the object.
 9. The computer program product ofclaim 8, wherein the instructions to modify an object detected withinthe user's field of view includes instructions to: determine that apriority score of the object is below a predetermined threshold; andremove the object from the user's field of view within the augmentedreality environment.
 10. The computer program product of claim 9,wherein the instructions to remove the object from the user's field ofview further includes instructions to: generate, using a generativeadversarial network, a portion of a background of the user's field ofview corresponding to an area encompassed by the object; and display thegenerated portion of the background corresponding to the areaencompassed by the object within the augmented reality environment. 11.The computer program product of claim 8, wherein the instructions tomodify an object detected within the user's field of view includesinstructions to: determine that a priority score of the object is abovea predetermined threshold; determine that a portion of the object isblocked from the user's view by another object having a priority scorebelow the predetermined threshold; generate, using a generativeadversarial network, the portion of the object blocked from the user'sview; and display the generated portion of the object blocked from theuser's view within the augmented reality environment.
 12. The computerprogram product of claim 8, wherein the instructions to modify anobjected detected within the user's field of view is further based, atleast in part, on instructions to: compare a classification of theobject to one or more object modification policies associated with theuser.
 13. The computer program product of claim 8, wherein theinstructions to generate a priority score for the one or more classifiedobjects is further based, at least in part, on: a distance between aclassified object and the user, a degree of visibility of an object, adetected motion of an object relative to the user, a perceived dangerassociated with an object, one or more detected precautions taken by theuser with respect to a classified object.
 14. The computer programproduct of claim 8, wherein an object is modified within the augmentedreality environment using a generative adversarial network.
 15. Acomputer system for multimedia modification, comprising: one or morecomputer processors; one or more computer readable storage media;computer program instructions; the computer program instructions beingstored on the one or more computer readable storage media for executionby the one or more computer processors; and the computer programinstructions including instructions to: classify one or more objectsdetected within a user's field of view through an augmented realityenvironment; determine a context of the user based, at least in part, onthe one or more classified objects detected within the user's field ofview; generate a priority score for the one or more classified objectsbased, at least in part, on the context of the user; and modify anobject detected within the user's field of view based, at least in part,on the priority score of the object.
 16. The computer system of claim15, wherein the instructions to modify an object detected within theuser's field of view includes instructions to: determine that a priorityscore of the object is below a predetermined threshold; and remove theobject from the user's field of view within the augmented realityenvironment.
 17. The computer system of claim 16, wherein theinstructions to remove the object from the user's field of view furtherincludes instructions to: generate, using a generative adversarialnetwork, a portion of a background of the user's field of viewcorresponding to an area encompassed by the object; and display thegenerated portion of the background corresponding to the areaencompassed by the object within the augmented reality environment. 18.The computer system of claim 15, wherein the instructions to modify anobject detected within the user's field of view includes instructionsto: determine that a priority score of the object is above apredetermined threshold; determine that a portion of the object isblocked from the user's view by another object having a priority scorebelow the predetermined threshold; generate, using a generativeadversarial network, the portion of the object blocked from the user'sview; and display the generated portion of the object blocked from theuser's view within the augmented reality environment.
 19. The computersystem of claim 15, wherein the instructions to modify an objecteddetected within the user's field of view is further based, at least inpart, on instructions to: compare a classification of the object to oneor more object modification policies associated with the user.
 20. Thecomputer system of claim 15, wherein the instructions to generate apriority score for the one or more classified objects is further based,at least in part, on: a distance between a classified object and theuser, a degree of visibility of an object, a detected motion of anobject relative to the user, a perceived danger associated with anobject, one or more detected precautions taken by the user with respectto a classified object.